Authors

Matthew Pannell - Matt is from Northern Virginia. Specifically, Springfiled, Virginia. His favorite place to eat in blacksburg is either Cabo Fish Taco or Zeppoli’s. In his freetime, he enjoys playing video games and being outside. His favorite video game is Civilization and his favorite hike in Blacksburg is Bald Knob. Responsible for the Multiple Regression, kNN, Naive Bayes, and formatted the dashboard.

Jason Bruno Teceros - Jason is from Herndon, VA, which is in the Northern Virginia area. He is not too far from where I grew up. Says his favorite dish to eat is a Chicken and Lamb over Rice (mixed lamb over rice) from Italianos. In his free time he enjoys to play video games as well and to read. His favorite game is Call of Duty and his favorite author is Steven King. He wishes he can be more active and participate in sports like he did when he was in high school. Responsible for Rigde Regression, Logistic Regression, Project Background page of the dashboard, and helped with general dashboard formatting.

Porter Lin - I am from China. Currently my favorite place to eat is Perry place. It is near the DDS building. My next favorite is Chipotle. My favorite music genre is classical, but recently I began picking up other genres as well. I don’t have one specific favorite game since I often plays awesome games by small indie creators, but out of all games I play, I play Minecraft the most. I did LOESS regression and helped with formatting the dashboard.

Abstract

This dashboard analyzes global development trends from 2000 to 2022 using World Bank indicators across multiple different countries and a range of socioeconomic and infrastructure-related variables. We investigate this data through different research questions using both regression and classification techniques. Our analysis spans GDP per capita, income inequality (Gini index), unemployment, and others that will be discussed and addressed in this dashboard. We apply both regression and classification techniques to analyze this data, such as; Multiple Regression (non-linear), Ridge Regression fit, LOESS fit, kNN classification, Naive Bayes Classification, and Logistic Regression. Our results will be visualized using a variety of maps and plots that offer a clear insight to the global patterns and by comparison; answering our respective research questions.

Introduction

This project leverages socioeconomic and infrastructure indicators from the World Bank, spanning the years 2000 to 2022, to investigate global development patterns across a diverse set of countries. The dataset includes economic measures such as GDP per capita, inflation, and unemployment, as well as social indicators like life expectancy, school enrollment, and health expenditure. Additionally, it incorporates infrastructure and technology access metrics, such as electricity access, internet usage, and mobile phone subscriptions.

Using this data, we explore research questions related to the economic growth. Our methods include ridge regression to understand linear relationships, LOESS smoothing to uncover non-linear trends over time, and logistic regression for classification tasks—particularly focusing on modeling digital inclusion. The dashboard offers a visual and interactive means of understanding these complex, multidimensional relationships. The data used can be found here: https://drive.google.com/drive/u/1/folders/16j7E2yBUDPmfGM00o7rGVGaCsE9BYhnK. Additionally you can download the data yourself from the world bank website here: https://databank.worldbank.org/source/world-development-indicators.

Data Dictionary

Research Question

Can we predict GDP per capita of a country in USD from total enrollment in tertiary education, how much a country spend on healthcare per capita, and the percentage of the population that lives in an urban area?

Original Data

Transformed Data

Fit Model

Call:
lm(formula = log_gdp ~ school + log_health + sqrt_urban, data = train_mlr)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.21562 -0.13358 -0.01252  0.14266  0.98148 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  3.8304094  0.0699193  54.783   <2e-16 ***
school      -0.0003958  0.0005025  -0.788    0.431    
log_health   0.8383025  0.0093836  89.337   <2e-16 ***
sqrt_urban  -0.0082864  0.0119256  -0.695    0.487    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.2519 on 780 degrees of freedom
Multiple R-squared:  0.9637,    Adjusted R-squared:  0.9636 
F-statistic:  6901 on 3 and 780 DF,  p-value: < 2.2e-16

The model explains about 96% of the variation in \(\log\) GDP per capita, which means the three predictors together do a very good job of accounting for differences in GDP across countries. The F-test result shows that the overall model is statistically significant.

Looking at the coefficients, log_health has a strong positive effect on GDP, while school has a very small negative effect. The coefficient for sqrt_urban is also close to zero, suggesting it doesn’t have much impact in this model.

Studentized Residual Plot

Leverage Plots

Cook’s Distance

QQ Plot

Fit Testing Data

Conclusion

Yes, we can predict GDP per capita of a country from total enrollment in tertiary education, how much a country spend on healthcare per capita, and the percentage of the population that lives in an urban area. In fact our model has a very good R^2 value and does a good job at this task. Overall the linear regression assumptions were upheld, but further research could be done on the problematic leverage and influence points. Based on the model’s performance, it seems like linear regression is fine for this task but further work could be done using robust regression methods to deal with the leverage and influential points.

Research Question and Background

To what extent can a country’s GDP per capita be predicted by its inflation rate, unemployment, export activity and capital formation?

Why use Ridge: You have multiple potentially correlated predictors (e.g., inflation and unemployment) so Ridge helps control for multicollinearity while identify which economic indicators best explain GDP per capita variation.

Choose Best Lambda

Ridge Regression Model

Research Question and Background

We aim to use LOESS to predict a country’s GDP per capita for several countries. We use both degree-1 and degree-2 polynomials to compare how good the fit was. This is ultimately determined by comparing their MSEs. A lower MSE indicates a better fit, but we need to take care of overfitting issues. In all analyses, the span is set to 0.5.

We chose 6 countries for the analyses. The full data range is from 2000 to 2021, but some countries may have data missing for some years.

United Kingdom

Uruguay

China

France

Indonesia

Colombia

Conclusion

From the analyses above, we can see that LOESS tends to have greater MSE when there is more fluctuation in a country’s growth, as in the case for the UK. For Uruguay and China, which showed a more graduate growth, the MSE is lower. Usually, we expect a degree-2 fit gave a lower MSE than a degree-1 fit, but for some countries like Indonesia and France, a degree-2 LOESS yielded a higher MSE than degree-1. The analyses here use a span of 0.5, but we should try out different span values to find out the optimal fit for each data. However, while a lower MSE indicates a good fit, we must consider the problem of overfitting. This ensures that when we introduce new data, LOESS can capture most of the features.

Research Question

Can we classify countries into high vs. low internet usage based on economic indicators such as GDP per capita of a country, total enrollment in tertiary education, how much a country spend on healthcare per capita, and the percentage of the population that lives in an urban area?

Set Up

For this research question we will create a binary variable based on if internet usage is greater than the median internet usage value. We will also prepare our data using min-max normalization. Lastly, we will divide the into a 70-30 training-testing split.

kNN Model

Choosing the Best k

As seen on the plot and table above, \(k=73\) and \(k=9\) are equal so we will just use \(k=7\) as the best \(k\) value to choose.

Conclusion

Yes, we can classify countries into high vs. low internet usage based on economic indicators such as GDP per capita of a country, total enrollment in tertiary education, how much a country spend on healthcare per capita, and the percentage of the population that lives in an urban area? Additionally, as seen in the analysis \(k=3\) performs the best for this task.

Research Question

Can we classify countries into high vs. low life expectancy based on development statistics like percent of the population that lives in an urban area, how much a country spend on healthcare per capita, and percent of the population who have access to electricity?

Set Up

For this research question, we will create a binary variable for high and low life expectancy based on if the life expectancy of a country is greater than the median life expectancy from the data. Additionally, we will create a 70-30 training-testing split.

Naive Bayes Model

Conclusion

Yes, we can classify countries into high vs. low internet usage based on economic indicators such as GDP per capita of a country, total enrollment in tertiary education, how much a country spend on healthcare per capita, and the percentage of the population that lives in an urban area. As seen in our analysis above, the Naive Bayes model performs very well on this task.

Research Question

How do life expectancy, income inequality, and unemployment influence the likelihood of a country being under developed?

Set Up

For this research question, we will define underdeveloped countries as those with life expectancy less than 70, and use this to create our classes.

Logistic Regression Model

Call:
glm(formula = low_expectancy ~ ., family = "binomial", data = logit_data)

Coefficients:
                                                                     Estimate
(Intercept)                                                         3.801e+00
`Unemployment, total (% of total labor force) (national estimate)` -1.079e-01
`Gini index`                                                       -5.121e-02
`GDP per capita (current US$)`                                     -6.504e-04
                                                                   Std. Error
(Intercept)                                                         6.835e-01
`Unemployment, total (% of total labor force) (national estimate)`  3.090e-02
`Gini index`                                                        1.552e-02
`GDP per capita (current US$)`                                      7.284e-05
                                                                   z value
(Intercept)                                                          5.562
`Unemployment, total (% of total labor force) (national estimate)`  -3.493
`Gini index`                                                        -3.300
`GDP per capita (current US$)`                                      -8.929
                                                                   Pr(>|z|)    
(Intercept)                                                        2.67e-08 ***
`Unemployment, total (% of total labor force) (national estimate)` 0.000478 ***
`Gini index`                                                       0.000965 ***
`GDP per capita (current US$)`                                      < 2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 770.24  on 1115  degrees of freedom
Residual deviance: 368.99  on 1112  degrees of freedom
AIC: 376.99

Number of Fisher Scoring iterations: 10